{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "096f2ee4",
   "metadata": {},
   "source": [
    "# Customizing Your Synapse Models\n",
    "\n",
    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/brainpy/blob/master/docs_version2/tutorial_building/customize_synapse_models.ipynb)\n",
    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/brainpy/brainpy/blob/master/docs_version2/tutorial_building/customize_synapse_models.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c1ae039",
   "metadata": {},
   "source": [
    "@[Chaoming Wang](https://github.com/chaoming0625) @[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bed1c4f",
   "metadata": {},
   "source": [
    "Synaptic computation is the core of brain dynamics programming. This is because in a real project most of the simulation time spends on the computation of synapses. In order to achieve efficient synaptic computation, BrainPy provides many useful supports. Here, we are going to explore the details of these supports."
   ]
  },
  {
   "cell_type": "code",
   "id": "1e518e11",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:38.605248Z",
     "start_time": "2025-10-06T03:54:34.967763Z"
    }
   },
   "source": [
    "import brainpy as bp\n",
    "import brainpy.math as bm\n",
    "\n",
    "bm.set_platform('cpu')\n",
    "\n",
    "bp.__version__"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.0.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "id": "f111708e",
   "metadata": {},
   "source": [
    "## Synapse Models in Math"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c5bbda2",
   "metadata": {},
   "source": [
    "Before we talk about the implementation of synapses in BrainPy, it's better to understand the targets (synapse models) we are going to implement. For different illustration purposes, we are going to implement two synapse models: [exponential synapse model](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.synapses.DualExpCOBA.html) and [AMPA synapse model](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.synapses.AMPA.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee864f9e",
   "metadata": {},
   "source": [
    "### 1. The exponential synapse model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "266c7fa7",
   "metadata": {},
   "source": [
    "The exponential synapse model assumes that once a pre-synaptic neuron generates a spike, the synaptic state arises instantaneously, then decays with a certain time constant $\\tau_{decay}$. Its dynamics is given by:\n",
    "\n",
    "$$\n",
    "\\frac{d g}{d t} = -\\frac{g}{\\tau_{decay}}+\\sum_{k} \\delta(t-D-t^{k})\n",
    "$$\n",
    "\n",
    "where $g$ is the synaptic state, $t^{k}$ is the spike time of the pre-synaptic neuron, and $D$ is the synaptic delay. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f30b788",
   "metadata": {},
   "source": [
    "Afterward, the current output onto the post-synaptic neuron is given in the conductance-based form:\n",
    "\n",
    "$$\n",
    "I_{syn}(t) = g_{max} g \\left( V-E \\right)\n",
    "$$\n",
    "\n",
    "where $E$ is the reversal potential of the synapse, $V$ is the post-synaptic membrane potential, $g_{max}$ is the maximum synaptic conductance. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7de41ac6",
   "metadata": {},
   "source": [
    "### 2. The AMPA synapse model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ffde7f",
   "metadata": {},
   "source": [
    "A classical model of AMPA synapse is to use the Markov process to model ion channel switch. Here $g$ represents the probability of channel opening, $1-g$ represents the probability of ion channel closing, and $\\alpha$ and $\\beta$ are the transition probability. Specifically, its formula is given by\n",
    "\n",
    "$$\n",
    "\\frac{dg}{dt} =\\alpha[T](1-g)-\\beta g\n",
    "$$\n",
    "\n",
    "where $\\alpha [T]$ denotes the transition probability from state $(1-g)$\n",
    "to state $(g)$; and $\\beta$ represents the transition probability of\n",
    "the other direction. $\\alpha$ is the binding constant. $\\beta$ is the\n",
    "unbinding constant. $[T]$ is the neurotransmitter concentration, and\n",
    "has the duration of 0.5 ms."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca0858af",
   "metadata": {},
   "source": [
    "Moreover, the post-synaptic current on the post-synaptic neuron is formulated as\n",
    "\n",
    "$$I_{syn} = g_{max} g (V-E)$$\n",
    "\n",
    "where $g_{max}$ is the maximum conductance, and $E$ is the reverse potential."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a8e0ffa",
   "metadata": {},
   "source": [
    "## Synapse Models in Silicon"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6c96d37",
   "metadata": {},
   "source": [
    "The implementation of synapse models is accomplished by ``brainpy.TwoEndConn`` interface. In this section, we talk about what supports are provided for the implementation of synapse models in silicon."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e5f55f7",
   "metadata": {},
   "source": [
    "### 1. ``brainpy.SynConn``"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa075a6",
   "metadata": {},
   "source": [
    "In BrainPy, `brainpy.SynConn` is used to model two-end synaptic computations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "297b0de9",
   "metadata": {},
   "source": [
    "To define a synapse model, two requirements should be satisfied:\n",
    "\n",
    "1\\. Constructor function ``__init__()``, in which three key arguments are needed.\n",
    "  - `pre`: the pre-synaptic neural group. It should be an instance of `brainpy.NeuGroup`.\n",
    "  - `post`: the post-synaptic neural group. It should be an instance of `brainpy.NeuGroup`.\n",
    "  - `conn` (optional): the connection type between these two groups. BrainPy has provided abundant connection types that are described in details in the [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb).\n",
    "\n",
    "2\\. Update function ``update(tdi)`` describes the updating rule from the current time $\\mathrm{tdi.t}$ to the next time $\\mathrm{tdi.t + tdi.dt}$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0f5d5a8",
   "metadata": {},
   "source": [
    "### 2. Variable delays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e9c232a",
   "metadata": {},
   "source": [
    "As seen in the above two synapse models, synaptic computations are usually involved with variable delays. A delay time (typically 0.3–0.5 ms) is usually required for a neurotransmitter to be released from a pre-synaptic membrane, diffuse across the synaptic cleft, and bind to a receptor site on the post-synaptic membrane.\n",
    "\n",
    "BrainPy provides several kinds of delay variables for users, including:\n",
    "\n",
    "- ``brainpy.math.LengthDelay``: a delay variable which defines a constant steps for delay.\n",
    "- ``brainpy.math.TimeDelay``: a delay variable which defines a constant time length for delay."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ce963de",
   "metadata": {},
   "source": [
    "Assume here we need a delay variable which has 1 ms delay. If the numerical integration precision ``dt`` is 0.1 ms, then we can create a ``brainpy.math.LengthDelay`` which has  10 delay time steps."
   ]
  },
  {
   "cell_type": "code",
   "id": "b9ced2ed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:38.777227Z",
     "start_time": "2025-10-06T03:54:38.621599Z"
    }
   },
   "source": [
    "target_data_to_delay = bm.Variable(bm.zeros(10))\n",
    "\n",
    "example_delay = bm.LengthDelay(target_data_to_delay,\n",
    "                               delay_len=10)  # delay 10 steps"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "id": "c602863d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:39.108918Z",
     "start_time": "2025-10-06T03:54:38.784748Z"
    }
   },
   "source": [
    "example_delay(5)  # call the delay data at 5 delay step"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "id": "7eaf0bf8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:39.134480Z",
     "start_time": "2025-10-06T03:54:39.116271Z"
    }
   },
   "source": [
    "example_delay(10)  # call the delay data at 10 delay step"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "id": "18fc175f",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "87519d2d",
   "metadata": {},
   "source": [
    "Alternatively, we can create an instance of ``brainpy.math.TimeDelay``, which use time ``t`` as the index to retrieve the delay data."
   ]
  },
  {
   "cell_type": "code",
   "id": "0f6ba901",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:39.204808Z",
     "start_time": "2025-10-06T03:54:39.140740Z"
    }
   },
   "source": [
    "t0 = 0.\n",
    "example_delay = bm.TimeDelay(target_data_to_delay,\n",
    "                             delay_len=1.0, t0=t0)  # delay 1.0 ms"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "id": "3039965c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:39.519134Z",
     "start_time": "2025-10-06T03:54:39.231410Z"
    }
   },
   "source": [
    "example_delay(t0 - 1.0)  # the delay data at t-1. ms"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "19b83ad3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:39.628952Z",
     "start_time": "2025-10-06T03:54:39.527641Z"
    }
   },
   "source": [
    "example_delay(t0 - 0.5)  # the delay data at t-0.5 ms"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "id": "a0a2bf84",
   "metadata": {},
   "source": [
    "### 3. Synaptic connections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f83608c5",
   "metadata": {},
   "source": [
    "Synaptic computations usually need to create connection between groups. BrainPy provides many wonderful supports to construct [synaptic connections](./synaptic_connections.ipynb). Simply speaking, ``brainpy.conn.Connector`` can create various data structures you want through the ``require()`` function. Take the random connection ``brainpy.conn.FixedProb`` which will be used in follows as the example,"
   ]
  },
  {
   "cell_type": "code",
   "id": "61de48c2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:39.678487Z",
     "start_time": "2025-10-06T03:54:39.648678Z"
    }
   },
   "source": [
    "example_conn = bp.conn.FixedProb(0.2)(pre_size=(5,), post_size=(8, ))"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "id": "88b50ec8",
   "metadata": {},
   "source": [
    "we can require the connection matrix (has the shape of ``(num_pre, num_post)``:"
   ]
  },
  {
   "cell_type": "code",
   "id": "b8e2ac09",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:39.924905Z",
     "start_time": "2025-10-06T03:54:39.688108Z"
    }
   },
   "source": [
    "example_conn.require('conn_mat')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[False,  True, False, False, False, False, False,  True],\n",
       "       [False, False,  True, False, False, False, False, False],\n",
       "       [False,  True, False, False, False, False, False, False],\n",
       "       [False,  True, False, False, False, False, False, False],\n",
       "       [False, False, False, False, False,  True, False, False]],      dtype=bool)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "id": "dff17faf",
   "metadata": {},
   "source": [
    "we can also require the connected indices of pre-synaptic neurons (``pre_ids``) and post-synaptic neurons (``post_ids``):"
   ]
  },
  {
   "cell_type": "code",
   "id": "3344a58d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:43.421360Z",
     "start_time": "2025-10-06T03:54:39.933172Z"
    }
   },
   "source": [
    "example_conn.require('pre_ids', 'post_ids')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Array([0, 1, 2, 3, 4], dtype=int32), Array([0, 6, 1, 6, 6], dtype=int32))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "id": "28e86024",
   "metadata": {},
   "source": [
    "Or, we can require the connection structure of ``pre2post`` which stores the information how does each pre-synaptic neuron connect to post-synaptic neurons:"
   ]
  },
  {
   "cell_type": "code",
   "id": "8db2a319",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:45.963691Z",
     "start_time": "2025-10-06T03:54:45.355746Z"
    }
   },
   "source": [
    "example_conn.require('pre2post')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Array([3, 2, 0, 3, 6], dtype=int32), Array([0, 1, 2, 3, 4, 5], dtype=int32))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "id": "ed1afff8",
   "metadata": {},
   "source": [
    "```{warning}\n",
    "Every `require()` function will establish a new connection pattern, and return the data structure users have required. Therefore any two `require()` will return different connection pattern, just like the examples above. Please keep in mind to require all the data structure at once if users want a consistent connection pattern.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44fa4941",
   "metadata": {},
   "source": [
    "More details of the connection structures please see the tutorial of [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc2af88d",
   "metadata": {},
   "source": [
    "### Achieving efficient synaptic computation is difficult"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ecabe94",
   "metadata": {},
   "source": [
    "Synaptic computations usually need to transform the data of the pre-synaptic dimension into the data of the post-synaptic dimension, or the data with the shape of the synapse number. There does not exist a universal computation method that are efficient in all cases. Usually, we need different ways for different connection situations to achieve efficient synaptic computation. In the next two sections, we will talk about how to define efficient synaptic models when your connections are **sparse** or **dense**.   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e494598",
   "metadata": {},
   "source": [
    "Before we start, we need to define some useful helper functions to define and show synapse models. Then, we will highlight the key differences of model definition when using different synaptic connections."
   ]
  },
  {
   "cell_type": "code",
   "id": "bd522429",
   "metadata": {
    "code_folding": [],
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:45.977913Z",
     "start_time": "2025-10-06T03:54:45.972090Z"
    }
   },
   "source": [
    "# Basic Model to define the exponential synapse model. This class \n",
    "# defines the basic parameters, variables, and integral functions. \n",
    "\n",
    "\n",
    "class BaseExpSyn(bp.dyn.SynConn):\n",
    "  def __init__(self, pre, post, conn, g_max=1., delay=0., tau=8.0, E=0., method='exp_auto'):\n",
    "    super(BaseExpSyn, self).__init__(pre=pre, post=post, conn=conn)\n",
    "\n",
    "    # check whether the pre group has the needed attribute: \"spike\"\n",
    "    self.check_pre_attrs('spike')\n",
    "\n",
    "    # check whether the post group has the needed attribute: \"input\" and \"V\"\n",
    "    self.check_post_attrs('input', 'V')\n",
    "\n",
    "    # parameters\n",
    "    self.E = E\n",
    "    self.tau = tau\n",
    "    self.delay = delay\n",
    "    self.g_max = g_max\n",
    "\n",
    "    # use \"LengthDelay\" to store the spikes of the pre-synaptic neuron group\n",
    "    self.delay_step = int(delay/bm.get_dt())\n",
    "    self.pre_spike = bm.LengthDelay(pre.spike, self.delay_step)\n",
    "\n",
    "    # integral function\n",
    "    self.integral = bp.odeint(lambda g, t: -g / self.tau, method=method)"
   ],
   "outputs": [],
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "id": "0d47e7ef",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:46.001660Z",
     "start_time": "2025-10-06T03:54:45.995024Z"
    }
   },
   "source": [
    "# Basic Model to define the AMPA synapse model. This class \n",
    "# defines the basic parameters, variables, and integral functions. \n",
    "\n",
    "\n",
    "class BaseAMPASyn(bp.dyn.SynConn):\n",
    "  def __init__(self, pre, post, conn, delay=0., g_max=0.42, E=0., alpha=0.98,\n",
    "               beta=0.18, T=0.5, T_duration=0.5, method='exp_auto'):\n",
    "    super(BaseAMPASyn, self).__init__(pre=pre, post=post, conn=conn)\n",
    "\n",
    "    # check whether the pre group has the needed attribute: \"spike\"\n",
    "    self.check_pre_attrs('spike')\n",
    "\n",
    "    # check whether the post group has the needed attribute: \"input\" and \"V\"\n",
    "    self.check_post_attrs('input', 'V')\n",
    "\n",
    "    # parameters\n",
    "    self.delay = delay\n",
    "    self.g_max = g_max\n",
    "    self.E = E\n",
    "    self.alpha = alpha\n",
    "    self.beta = beta\n",
    "    self.T = T\n",
    "    self.T_duration = T_duration\n",
    "\n",
    "    # use \"LengthDelay\" to store the spikes of the pre-synaptic neuron group\n",
    "    self.delay_step = int(delay/bm.get_dt())\n",
    "    self.pre_spike = bm.LengthDelay(pre.spike, self.delay_step)\n",
    "\n",
    "    # store the arrival time of the pre-synaptic spikes\n",
    "    self.spike_arrival_time = bm.Variable(bm.ones(self.pre.num) * -1e7)\n",
    "\n",
    "    # integral function\n",
    "    self.integral = bp.odeint(self.derivative, method=method)\n",
    "\n",
    "  def derivative(self, g, t, TT):\n",
    "    dg = self.alpha * TT * (1 - g) - self.beta * g\n",
    "    return dg"
   ],
   "outputs": [],
   "execution_count": 13
  },
  {
   "cell_type": "code",
   "id": "d3640a4a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:46.014952Z",
     "start_time": "2025-10-06T03:54:46.009167Z"
    }
   },
   "source": [
    "# for more details of how to run a simulation please see the tutorials in \"Dynamics Simulation\"\n",
    "\n",
    "def show_syn_model(model):\n",
    "  pre = bp.neurons.LIF(1, V_rest=-60., V_reset=-60., V_th=-40.)\n",
    "  post = bp.neurons.LIF(1, V_rest=-60., V_reset=-60., V_th=-40.)\n",
    "  syn = model(pre, post, conn=bp.conn.One2One())\n",
    "  net = bp.Network(pre=pre, post=post, syn=syn)\n",
    "\n",
    "  runner = bp.DSRunner(net,\n",
    "                       monitors=['pre.V', 'post.V', 'syn.g'],\n",
    "                       inputs=['pre.input', 22.])\n",
    "  runner.run(100.)\n",
    "\n",
    "  fig, gs = bp.visualize.get_figure(1, 2, 3, 4)\n",
    "  fig.add_subplot(gs[0, 0])\n",
    "  bp.visualize.line_plot(runner.mon.ts, runner.mon['syn.g'], legend='syn.g')\n",
    "  fig.add_subplot(gs[0, 1])\n",
    "  bp.visualize.line_plot(runner.mon.ts, runner.mon['pre.V'], legend='pre.V')\n",
    "  bp.visualize.line_plot(runner.mon.ts, runner.mon['post.V'], legend='post.V', show=True)"
   ],
   "outputs": [],
   "execution_count": 14
  },
  {
   "cell_type": "markdown",
   "id": "dde06bd8",
   "metadata": {},
   "source": [
    "## Computation with Dense Connections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e5abebb",
   "metadata": {},
   "source": [
    "Matrix-based synaptic computation is straightforward. Especially, when your models are connected densely, using matrix is highly efficient. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "984c65a4",
   "metadata": {},
   "source": [
    "### ``conn_mat``"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a5bad33",
   "metadata": {},
   "source": [
    "Assume two neuron groups are connected through a fixed probability of 0.7. "
   ]
  },
  {
   "cell_type": "code",
   "id": "102c71e7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:46.024447Z",
     "start_time": "2025-10-06T03:54:46.019735Z"
    }
   },
   "source": [
    "conn = bp.conn.FixedProb(0.7)(pre_size=6, post_size=8)"
   ],
   "outputs": [],
   "execution_count": 15
  },
  {
   "cell_type": "markdown",
   "id": "5a791b6c",
   "metadata": {},
   "source": [
    "Then you can create the connection matrix though ``conn.require(\"conn_mat\")``:"
   ]
  },
  {
   "cell_type": "code",
   "id": "4bbb027f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:46.161274Z",
     "start_time": "2025-10-06T03:54:46.031776Z"
    }
   },
   "source": [
    "conn.require('conn_mat')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[ True,  True,  True, False, False,  True,  True,  True],\n",
       "       [False,  True, False, False,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True,  True,  True,  True,  True],\n",
       "       [False, False,  True,  True,  True, False,  True,  True]],      dtype=bool)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "id": "c925c9f4",
   "metadata": {},
   "source": [
    "``conn_mat`` has the shape of ``(num_pre, num_post)``. Therefore, transforming the data with the pre-synaptic dimension into the date of the post-synaptic dimension is very easy. You just need make a matrix multiplication: ``brainpy.math.dot(pre_values,  conn_mat)`` ($\\mathbb{R}^\\mathrm{num\\_pre} @ \\mathbb{R}^\\mathrm{(num\\_pre, num\\_post)} \\to \\mathbb{R}^\\mathrm{num\\_post}$). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c2553fc",
   "metadata": {},
   "source": [
    "With the synaptic connection of ``conn_mat`` in above, we can define the **exponential synapse model** as the follows. It's worthy to note that the evolution of states ouput onto the same post-synaptic neurons in exponential synapses can be superposed. This means we can declare the synapse variables with the shape of post-synaptic group, rather than the number of the total synapses. "
   ]
  },
  {
   "cell_type": "code",
   "id": "b8e7b088",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:46.186980Z",
     "start_time": "2025-10-06T03:54:46.182354Z"
    }
   },
   "source": [
    "class ExpConnMat(BaseExpSyn):\n",
    "  def __init__(self, *args, **kwargs):\n",
    "    super(ExpConnMat, self).__init__(*args, **kwargs)\n",
    "\n",
    "    # connection matrix\n",
    "    self.conn_mat = self.conn.require('conn_mat').astype(float)\n",
    "\n",
    "    # synapse gating variable\n",
    "    # -------\n",
    "    # NOTE: Here the synapse number is the same with \n",
    "    #       the post-synaptic neuron number. This is \n",
    "    #       different from the AMPA synapse.\n",
    "    self.g = bm.Variable(bm.zeros(self.post.num))\n",
    "\n",
    "  def update(self, tdi, x=None):\n",
    "    _t, _dt = tdi.t, tdi.dt\n",
    "    # pull the delayed pre spikes for computation\n",
    "    delayed_spike = self.pre_spike(self.delay_step)\n",
    "    # push the latest pre spikes into the bottom\n",
    "    self.pre_spike.update(self.pre.spike)\n",
    "    # integrate the synapse state\n",
    "    self.g.value = self.integral(self.g, _t, dt=_dt)\n",
    "    # update synapse states according to the pre spikes\n",
    "    post_sps = bm.dot(delayed_spike.astype(float), self.conn_mat)\n",
    "    self.g += post_sps\n",
    "    # get the post-synaptic current\n",
    "    self.post.input += self.g_max * self.g * (self.E - self.post.V)"
   ],
   "outputs": [],
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "id": "4acb4081",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:46.888560Z",
     "start_time": "2025-10-06T03:54:46.196936Z"
    }
   },
   "source": [
    "show_syn_model(ExpConnMat)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\codes\\projects\\BrainPy\\brainpy\\version2\\dynsys.py:325: UserWarning: \n",
      "From brainpy>=2.4.3, update() function no longer needs to receive a global shared argument.\n",
      "\n",
      "Instead of using:\n",
      "\n",
      "  def update(self, tdi, *args, **kwagrs):\n",
      "     t = tdi['t']\n",
      "     ...\n",
      "\n",
      "Please use:\n",
      "\n",
      "  def update(self, *args, **kwagrs):\n",
      "     t = bp.share['t']\n",
      "     ...\n",
      "\n",
      "  warnings.warn(_update_deprecate_msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "415c4ba120d341918c1a2f626099714a"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 18
  },
  {
   "cell_type": "markdown",
   "id": "1eb27017",
   "metadata": {},
   "source": [
    "We can also use ``conn_mat`` to define an **AMPA synapse model**. Note here the shape of the synapse variable $g$ is ``(num_pre, num_post)``, rather than ``self.post.num`` in the above exponential synapse model. This is because the synaptic states of AMPA model can not be superposed. "
   ]
  },
  {
   "cell_type": "code",
   "id": "37736f86",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:46.901999Z",
     "start_time": "2025-10-06T03:54:46.896334Z"
    }
   },
   "source": [
    "class AMPAConnMat(BaseAMPASyn):\n",
    "  def __init__(self, *args, **kwargs):\n",
    "    super(AMPAConnMat, self).__init__(*args, **kwargs)\n",
    "\n",
    "    # connection matrix\n",
    "    self.conn_mat = self.conn.require('conn_mat').astype(float)\n",
    "\n",
    "    # synapse gating variable\n",
    "    # -------\n",
    "    # NOTE: Here the synapse shape is (num_pre, num_post),\n",
    "    #       in contrast to the ExpConnMat\n",
    "    self.g = bm.Variable(bm.zeros((self.pre.num, self.post.num)))\n",
    "\n",
    "  def update(self, tdi, x=None):\n",
    "    _t, _dt = tdi.t, tdi.dt\n",
    "    # pull the delayed pre spikes for computation\n",
    "    delayed_spike = self.pre_spike(self.delay_step)\n",
    "    # push the latest pre spikes into the bottom\n",
    "    self.pre_spike.update(self.pre.spike)\n",
    "    # get the time of pre spikes arrive at the post synapse\n",
    "    self.spike_arrival_time.value = bm.where(delayed_spike, _t, self.spike_arrival_time)\n",
    "    # get the neurotransmitter concentration at the current time\n",
    "    TT = ((_t - self.spike_arrival_time) < self.T_duration) * self.T\n",
    "    # integrate the synapse state\n",
    "    TT = TT.reshape((-1, 1)) * self.conn_mat  # NOTE: only keep the concentrations\n",
    "                                              #       on the invalid connections\n",
    "    self.g.value = self.integral(self.g, _t, TT, dt=_dt)\n",
    "    # get the post-synaptic current\n",
    "    g_post = self.g.sum(axis=0)\n",
    "    self.post.input += self.g_max * g_post * (self.E - self.post.V)"
   ],
   "outputs": [],
   "execution_count": 19
  },
  {
   "cell_type": "code",
   "id": "fab4f7cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:47.298398Z",
     "start_time": "2025-10-06T03:54:46.921151Z"
    }
   },
   "source": [
    "show_syn_model(AMPAConnMat)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "47cef6dbcae04ccda68a9ee2a9076848"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 20
  },
  {
   "cell_type": "markdown",
   "id": "e1a02e48",
   "metadata": {},
   "source": [
    "### Special connections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69362ac5",
   "metadata": {},
   "source": [
    "Sometimes, we can define some synapse models with special connection types, such as all-to-all connection, or one-to-one connection. For these special situations, even the connection information can be ignored, i.e., we do not need ``conn_mat`` or other structures anymore."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b3f691",
   "metadata": {},
   "source": [
    "Assume the pre-synaptic group connects to the post-synaptic group with a all-to-all fashion. \n",
    "Then, exponential synapse model can be defined as, "
   ]
  },
  {
   "cell_type": "code",
   "id": "b41ef340",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:47.350770Z",
     "start_time": "2025-10-06T03:54:47.345414Z"
    }
   },
   "source": [
    "class ExpAll2All(BaseExpSyn):\n",
    "  def __init__(self, *args, **kwargs):\n",
    "    super(ExpAll2All, self).__init__(*args, **kwargs)\n",
    "\n",
    "    # synapse gating variable\n",
    "    # -------\n",
    "    # The synapse variable has the shape of the post-synaptic group\n",
    "    self.g = bm.Variable(bm.zeros(self.post.num))\n",
    "\n",
    "  def update(self, tdi, x=None):\n",
    "    _t, _dt = tdi.t, tdi.dt\n",
    "    delayed_spike = self.pre_spike(self.delay_step)\n",
    "    self.pre_spike.update(self.pre.spike)\n",
    "    self.g.value = self.integral(self.g, _t, dt=_dt)\n",
    "    self.g += delayed_spike.sum()  # NOTE: HERE is the difference\n",
    "    self.post.input += self.g_max * self.g * (self.E - self.post.V)"
   ],
   "outputs": [],
   "execution_count": 21
  },
  {
   "cell_type": "code",
   "id": "d1f3cca3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:47.679338Z",
     "start_time": "2025-10-06T03:54:47.381630Z"
    }
   },
   "source": [
    "show_syn_model(ExpAll2All)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "2232d948a8414578a03665a8f9b5086f"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 22
  },
  {
   "cell_type": "markdown",
   "id": "d37e8b1d",
   "metadata": {},
   "source": [
    "Similarly, the AMPA synapse model can be defined as"
   ]
  },
  {
   "cell_type": "code",
   "id": "01ce8789",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:47.693148Z",
     "start_time": "2025-10-06T03:54:47.688231Z"
    }
   },
   "source": [
    "class AMPAAll2All(BaseAMPASyn):\n",
    "  def __init__(self, *args, **kwargs):\n",
    "    super(AMPAAll2All, self).__init__(*args, **kwargs)\n",
    "\n",
    "    # synapse gating variable\n",
    "    # -------\n",
    "    # The synapse variable has the shape of the post-synaptic group\n",
    "    self.g = bm.Variable(bm.zeros((self.pre.num, self.post.num)))\n",
    "\n",
    "  def update(self, tdi, x=None):\n",
    "    _t, _dt = tdi.t, tdi.dt\n",
    "    delayed_spike = self.pre_spike(self.delay_step)\n",
    "    self.pre_spike.update(self.pre.spike)\n",
    "    self.spike_arrival_time.value = bm.where(delayed_spike, _t, self.spike_arrival_time)\n",
    "    TT = ((_t - self.spike_arrival_time) < self.T_duration) * self.T\n",
    "    TT = TT.reshape((-1, 1))  # NOTE: here is the difference\n",
    "    self.g.value = self.integral(self.g, _t, TT, dt=_dt)\n",
    "    g_post = self.g.sum(axis=0) # NOTE: here is also different\n",
    "    self.post.input += self.g_max * g_post * (self.E - self.post.V)"
   ],
   "outputs": [],
   "execution_count": 23
  },
  {
   "cell_type": "code",
   "id": "51a07101",
   "metadata": {
    "lines_to_next_cell": 1,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:48.049636Z",
     "start_time": "2025-10-06T03:54:47.712187Z"
    }
   },
   "source": [
    "show_syn_model(AMPAAll2All)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d0464c7bec914aeaaea632ad509bd60c"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 24
  },
  {
   "cell_type": "markdown",
   "id": "8eb7c494",
   "metadata": {},
   "source": [
    "Actually, the synaptic computation with these special connections can be very efficient! A concrete example please see a [decision making spiking model](https://brainpy-examples.readthedocs.io/en/latest/decision_making/Wang_2002_decision_making_spiking.html) in BrainPy-Examples. This implementation achieve at least four times acceleration comparing to the implementation in other frameworks."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d819b14f",
   "metadata": {},
   "source": [
    "## Computation with Sparse Connections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d0e7131",
   "metadata": {},
   "source": [
    "However, in the real neural system, the neurons are connected **sparsely** in essence. \n",
    "\n",
    "Imaging you want to connect 10,000 pre-synaptic neurons to 10,000 post-synaptic neurons with a 10% random connection probability. Using matrix, you need $10^8$ floats to save the synaptic state, and at each update step, you need do computation on $10^8$ floats. Actually, the number of synapses you really connect is only $10^7$. See, there is a huge memory waste and computing resource inefficiency. Moreover, at the given time $\\mathrm{\\_t}$, the number of pre-synaptic neurons in the spiking state is small on average. This means we have made many useless computations when defining synaptic computations with matrix-based connections (zeros dot connection matrix results in zeros).\n",
    "\n",
    "Therefore, we need new ways to define synapse models. Specifically, we use vectors to store the connected neuron indices, like the ``pre_ids`` and ``post_ids`` (see [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb)). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b67256b8",
   "metadata": {},
   "source": [
    "In the below, we assume you have learned the synaptic connection types detailed in the tutorial of [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4806dc08",
   "metadata": {},
   "source": [
    "### The ``pre2post`` operator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "882dd9de",
   "metadata": {},
   "source": [
    "A notable difference of brain dynamics models from the deep learning is that they are sparse and event-driven. In order to support this significant different kind of computations, BrainPy has built many useful [operators](../apis/auto/math/operators.rst). In this section, we talk about a set of operators needed in ``pre2post`` computations. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "059255e0",
   "metadata": {},
   "source": [
    "Note before we have said that exponential synapse model can make computations at the dimension of the post-synaptic group. Therefore, we can directly transform the pre-synaptic data into the data of the post-synaptic shape. [brainpy.math.pre2post_event_sum(events, pre2post, post_num, values)](../apis/auto/math/generated/brainpy.math.operators.pre2post_event_sum.rst) can satisfy your requirements. This operator needs the synaptic structure of ``pre2post`` (a tuple contains the ``post_ids`` and ``idnptr`` of pre-synaptic neurons). \n",
    "\n",
    "If ``values`` is a scalar, ``pre2post_event_sum`` is equivalent to:\n",
    "\n",
    "```python\n",
    "post_val = np.zeros(post_num)\n",
    "\n",
    "post_ids, idnptr = pre2post\n",
    "for i in range(pre_num):\n",
    "  if events[i]:\n",
    "    for j in range(idnptr[i], idnptr[i+1]):\n",
    "      post_val[post_ids[i]] += values\n",
    "```\n",
    "\n",
    "If ``values`` is a vector, ``pre2post_event_sum`` is equivalent to:\n",
    "\n",
    "```python\n",
    "post_val = np.zeros(post_num)\n",
    "\n",
    "post_ids, idnptr = pre2post\n",
    "for i in range(pre_num):\n",
    "  if events[i]:\n",
    "    for j in range(idnptr[i], idnptr[i+1]):\n",
    "      post_val[post_ids[i]] += values[j]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff96270d",
   "metadata": {},
   "source": [
    "With this operator, exponential synapse model can be defined as:"
   ]
  },
  {
   "cell_type": "code",
   "id": "94d26b81",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:48.088270Z",
     "start_time": "2025-10-06T03:54:48.084010Z"
    }
   },
   "source": [
    "class ExpSparse(BaseExpSyn):\n",
    "  def __init__(self, *args, **kwargs):\n",
    "    super(ExpSparse, self).__init__(*args, **kwargs)\n",
    "\n",
    "    # connections\n",
    "    self.pre2post = self.conn.require('pre2post')\n",
    "\n",
    "    # synapse variable\n",
    "    self.g = bm.Variable(bm.zeros(self.post.num))\n",
    "\n",
    "  def update(self, tdi, x=None):\n",
    "    _t, _dt = tdi.t, tdi.dt\n",
    "    delayed_spike = self.pre_spike(self.delay_step)\n",
    "    self.pre_spike.update(self.pre.spike)\n",
    "    self.g.value = self.integral(self.g, _t, dt=_dt)\n",
    "    # NOTE: update synapse states according to the pre spikes\n",
    "    post_sps = bm.pre2post_event_sum(delayed_spike, self.pre2post, self.post.num, 1.)\n",
    "    self.g += post_sps\n",
    "    self.post.input += self.g_max * self.g * (self.E - self.post.V)"
   ],
   "outputs": [],
   "execution_count": 25
  },
  {
   "cell_type": "code",
   "id": "afd6a770",
   "metadata": {
    "lines_to_next_cell": 1,
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:48.785826Z",
     "start_time": "2025-10-06T03:54:48.121043Z"
    }
   },
   "source": [
    "show_syn_model(ExpSparse)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "7be2ff63883146478ebf5d47ee5c61d6"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 26
  },
  {
   "cell_type": "markdown",
   "id": "eed2af26",
   "metadata": {},
   "source": [
    "This model will be very efficient when your synapses are connected sparsely. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6300cda5",
   "metadata": {},
   "source": [
    "### The ``pre2syn`` and ``syn2post`` operators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f39c2f8",
   "metadata": {},
   "source": [
    "However, for AMPA synapse model, the pre-synaptic values can not be directly transformed into the post-synaptic dimensional data. Therefore, we need to first change the pre data into the data of the synapse dimension, then transform the synapse-dimensional data into the post-dimensional data. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae7c55b3",
   "metadata": {},
   "source": [
    "Therefore, the core problem of synaptic computation is how to convert values among different shape of arrays. Specifically, in the above AMPA synapse model, we have three kinds of array shapes (see the following figure): arrays with the dimension of pre-synaptic group, arrays of the dimension of post-synaptic group, and arrays with the shape of synaptic connections. Converting the pre-synaptic spiking state into the synaptic state and grouping the synaptic variable as the post-synaptic current value are central problems of synaptic computation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89a546a3",
   "metadata": {},
   "source": [
    "![](../_static/pre2syn2post.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4aeef36",
   "metadata": {},
   "source": [
    "Here BrainPy provides two operators [brainpy.math.pre2syn(pre_values, pre_ids)](../apis/auto/math/generated/brainpy.math.operators.pre2syn.rst) and [brainpy.math.syn2post(syn_values, post_ids, post_num)](../apis/auto/math/generated/brainpy.math.operators.syn2post.rst) to convert vectors among different dimensions.\n",
    "\n",
    "- ``brainpy.math.pre2syn()`` receives two arguments: \"pre_values\" (the variable of the pre-synaptic dimension) and \"pre_ids\" (the connected pre-synaptic neuron index).\n",
    "- ``brainpy.math.syn2post()`` receives three arguments: \"syn_values\" (the variable with the synaptic size), \"post_ids\" (the connected post-synaptic neuron index) and \"post_num\" (the number of the post-synaptic neurons)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8400124a",
   "metadata": {},
   "source": [
    "Based on these two operators, we can define the AMPA synapse model as:"
   ]
  },
  {
   "cell_type": "code",
   "id": "fa62799e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:48.825344Z",
     "start_time": "2025-10-06T03:54:48.820247Z"
    }
   },
   "source": [
    "class AMPASparse(BaseAMPASyn):\n",
    "  def __init__(self, *args, **kwargs):\n",
    "    super(AMPASparse, self).__init__(*args, **kwargs)\n",
    "\n",
    "    # connection matrix\n",
    "    self.pre_ids, self.post_ids = self.conn.require('pre_ids', 'post_ids')\n",
    "\n",
    "    # synapse gating variable\n",
    "    # -------\n",
    "    # NOTE: Here the synapse shape is (num_syn,)\n",
    "    self.g = bm.Variable(bm.zeros(len(self.pre_ids)))\n",
    "\n",
    "  def update(self, tdi, x=None):\n",
    "    _t, _dt = tdi.t, tdi.dt\n",
    "    delayed_spike = self.pre_spike(self.delay_step)\n",
    "    self.pre_spike.update(self.pre.spike)\n",
    "    # get the time of pre spikes arrive at the post synapse\n",
    "    self.spike_arrival_time.value = bm.where(delayed_spike, _t, self.spike_arrival_time)\n",
    "    # get the arrival time with the synapse dimension\n",
    "    arrival_times = bm.pre2syn(self.spike_arrival_time, self.pre_ids)\n",
    "    # get the neurotransmitter concentration at the current time\n",
    "    TT = ((_t - arrival_times) < self.T_duration) * self.T\n",
    "    # integrate the synapse state\n",
    "    self.g.value = self.integral(self.g, _t, TT, dt=_dt)\n",
    "    # get the post-synaptic current\n",
    "    g_post = bm.syn2post(self.g, self.post_ids, self.post.num)\n",
    "    self.post.input += self.g_max * g_post * (self.E - self.post.V)"
   ],
   "outputs": [],
   "execution_count": 27
  },
  {
   "cell_type": "code",
   "id": "3ccfcf3b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:49.190728Z",
     "start_time": "2025-10-06T03:54:48.862216Z"
    }
   },
   "source": [
    "show_syn_model(AMPASparse)"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\codes\\projects\\BrainPy\\brainpy\\version2\\dynsys.py:325: UserWarning: \n",
      "From brainpy>=2.4.3, update() function no longer needs to receive a global shared argument.\n",
      "\n",
      "Instead of using:\n",
      "\n",
      "  def update(self, tdi, *args, **kwagrs):\n",
      "     t = tdi['t']\n",
      "     ...\n",
      "\n",
      "Please use:\n",
      "\n",
      "  def update(self, *args, **kwagrs):\n",
      "     t = bp.share['t']\n",
      "     ...\n",
      "\n",
      "  warnings.warn(_update_deprecate_msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "b0b9c57f5bce44e391caa2190b15cf49"
      }
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x300 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 28
  },
  {
   "cell_type": "markdown",
   "id": "92903cb0",
   "metadata": {},
   "source": [
    "We hope this tutorial will help users build synapse models in a more efficient way. "
   ]
  },
  {
   "cell_type": "code",
   "id": "b047b958",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T03:54:49.207333Z",
     "start_time": "2025-10-06T03:54:49.205232Z"
    }
   },
   "source": [],
   "outputs": [],
   "execution_count": null
  }
 ],
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